The success and impact of nearly every project in IDD hinges on the proper use of statistical techniques. Thus, Core E has a critical role in facilitating research for all IDDRC investigators, as well as for the progress of the other IDDRC Cores and Signature Research Project. Core E performs a unique function for IDDRC investigators as it helps them identify and use the statistical and methodological expertise and resources available at Vanderbilt University (VU) and Vanderbilt University Medical Center (VUMC) that are appropriate for their questions ? especially for more complicated research designs (e.g., many layers of nesting) or those with statistical limitations (e.g., small sample sizes common in research with rare populations). Further, through generative activity with Clinical Translational and Translational Neuroscience Cores B and C, Core E provides sophisticated and non-trivial statistical methods and models tailored to IDD-related scientific questions (e.g., Bayesian spatio-temporal models for neuroimaging analysis). In addition to having considerable expertise in biostatistics, neuro-statistics, and quantitative psychology, Vanderbilt is also a national leader in developing big data structures and mining that data to advance health and development research, including the Synthetic Derivative (SD), a de-identified dataset of electronic health record data collected from over ~2.8 million total records. Though such big data structures are incredible resources to Vanderbilt, and especially IDDRC investigators with their ability to capture large samples of rare disorders, it can be challenging to put the data in analyzable formats and select suitable statistical approaches for analysis. Core E enables IDDRC investigators to fully capitalize on all these VU/VUMC resources through three aims: Aim 1, which provides access to modern statistical and data science methods to answer questions of relevance to IDD, including conducting data analyses for the Signature IDDRC Research Project; Aim 2, which enhances training in IDD research for those engaging in data science methods, including implementing a novel internal training grant program between Data Sciences Institute trainees and the IDDRC; and Aim 3, which supports innovation in health- related IDD research by facilitating use of large data sets such as the SD, including providing cutting-edge consultations and tools for working with large-scale SD IDD-curated database that IDDRC investigators can use for generating pilot data and conducting studies. Collectively, Core E?s aims and generative work and interactions with other IDDRC Cores not only meets the immediate needs of IDDRC investigators, but also anticipates future ones, by allowing for novel resources, platforms, and methods to be developed. By tackling and solving complex, multi-modal data science questions, Core E is poised to contribute substantially over the next 5 years to accelerating scientific discovery to improve the outcomes of people with IDDs.